Senior Engineer, Machine Learning, Vision

Rockwell Automation is a global technology leader helping manufacturers become more productive, sustainable, and agile. The Senior Engineer will implement and ship machine learning capabilities for machine vision and visual inspection, owning model training, evaluation, deployment, accuracy, and latency in production. The role also involves building dataset pipelines, tuning edge inference, and improving models based on real customer data.

Responsibilities:

• Implement and ship machine learning capabilities in the product, from model training pipeline through the user-facing configuration workflow
• Build and maintain the training, labeling, and dataset management pipelines the product and the field depend on
• Develop evaluation suites and accuracy regression tests that gate model changes before release
• Tune models and inference pipelines to meet latency and throughput targets on edge hardware
• Debug model behavior against real customer data and turn field failures into dataset and evaluation improvements
• Participate in design, code, and model reviews, and contribute to the team's ML engineering practices

Requirements:

• Bachelor's Degree or Equivalent Years of Relevant Work Experience
• Typically requires 5+ years of related experience in a software product development environment
• Bachelor's or advanced degree in Computer Science, Electrical Engineering, or a related technical discipline
• Depth in PyTorch or TensorFlow and in modern computer vision architectures
• Experience with edge inference and model optimization - ONNX, TensorRT, quantization
• Experience building dataset and labeling pipelines, including handling class imbalance and label noise
• Familiarity with evaluation-driven workflows: offline metrics, regression suites, and production monitoring
• Exposure to industrial automation, machine vision, or manufacturing quality systems
• Hands-on experience building and training machine learning models in Python
• Experience deploying a model into a production system and supporting it afterward